Information workers (users) frequently work with documents, spreadsheets, databases and so forth containing mentions of various entities. For example, a spreadsheet user may have a list of camera models, or a database user may have a table of companies. The user may want additional information related to an entity or entities.
By way of example, a user may need to fill in additional information in order to complete a task. As a more particular example, to help in making a decision, a user may be tasked with augmenting a spreadsheet containing camera models, by filling in various attributes for each model such as brand, resolution, price and optical zoom. To complete such entity augmenting tasks today, users try to manually find web sources containing the desired information and merge the found corresponding data values with the existing data to assemble a complete set of data.
Other tasks related to augmenting data often come up as well. Thus in general, users may benefit from an automated solution that assists users in performing such tasks. However, existing approaches are generally unsatisfactory with respect to their level of data precision (the augmented data is often wrong) and recall (the augmented data is often unable to be found, e.g., due to poor coverage). It is thus desirable to provide an automated solution to these and other such tasks that assists users to a reasonably desirable extent.